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Erik Stafford

Erik Stafford joined the faculty at HBS in July 1999, where he has taught finance in the required and elective curricula of the MBA Program and in the CFA Investment Management Workshop.

Erik's research efforts focus on investment management, capital markets, and the financial system. Two of his papers have won annual prizes for research excellence. "Managerial Decisions and Long-Term Stock Price Performance," written with Mark Mitchell, won the Merton Miller Prize for the paper deemed most significant in the 2000 Journal of Business , and "Limited Arbitrage in Equity Markets," written with Mark Mitchell and Todd Pulvino, won the Smith-Breeden Prize for outstanding paper in the Journal of Finance.

Erik has an undergraduate degree in Finance and Economics from the University of Maryland and a Ph.D. in Finance from the University of Chicago's Graduate School of Business.

In addition to his work at Harvard, Erik is an advisor to SummerHaven Index Management. SummerHaven designs indexes based on academic and proprietary research.

Traditional risk factor models indicate that hedge funds capture pre-fee alphas of 6% to 10% per annum over the period from 1996 to 2012. At the same time, the hedge fund return series is not reliably distinguishable from the returns of mechanical S&P 500 put-writing strategies. We show that the high excess returns to hedge funds and put-writing are consistent with an equilibrium in which a small subset of investors specialize in bearing downside market risks. Required rates of return in such an equilibrium can dramatically exceed those suggested by traditional models, affecting inference about the attractiveness of these investments.

This paper investigates the spectacular rise and fall of structured finance. The essence of structured finance activities is the pooling of economic assets like loans, bonds, and mortgages, and the subsequent issuance of a prioritized capital structure of claims, known as tranches, against these collateral pools. As a result of the prioritization scheme used in structuring claims, many of the manufactured tranches are far safer than the average asset in the underlying pool. This ability of structured finance to repackage risks and to create "safe" assets from otherwise risky collateral led to a dramatic expansion in the issuance of structured securities, most of which were viewed by investors to be virtually risk-free and certified as such by the rating agencies. At the core of the recent financial market crisis has been the discovery that these securities are actually far riskier than originally advertised. We examine how the process of securitization allowed trillions of dollars of risky assets to be transformed into securities that were widely considered to be safe. We highlight two features of structured finance products—the extreme fragility of their ratings to modest imprecision in evaluating underlying risks, and their exposure to systematic risks—that go a long way in explaining the spectacular rise and fall of structured finance. We conclude with an assessment of what went wrong and the relative importance of rating agency errors, investor credulity, and perverse incentives and suspect behavior on the part of issuers, rating agencies, and borrowers.

We decompose bank activities into passive and active components and evaluate the performance of the active components of the bank business model by controlling for passive maturity transformation strategies that can be executed in the capital market. Over the period 1960–2016, we find that (1) unlevered bank assets underperform passive portfolios of maturity-matched U.S. Treasury bonds; (2) the cost of bank deposits exceeds the cost of bank debt; (3) bank equities have CAPM betas near one, while passive maturity transformation strategies have CAPM betas near zero; and (4) portfolios of bank equities consistently underperform portfolios designed to passively mimic their economic exposures. The very strong investment performance of passive maturity transformation strategies over this period may mask the underperformance of the specialized bank activities.

Private equity funds tend to select relatively small firms with low EBITDA multiples. Publicly traded equities with these characteristics have high risk-adjusted returns after controlling for common factors typically associated with value stocks. Hold-to-maturity accounting of portfolio net asset value eliminates the majority of measured risk. A passive portfolio of small, low EBITDA multiple stocks with modest amounts of leverage and hold-to-maturity accounting of net asset value produces an unconditional
return distribution that is highly consistent with that of the pre-fee aggregate private equity index. The passive replicating strategy represents an economically large improvement in risk- and liquidity-adjusted returns over direct allocations to private equity funds, which charge average fees of 6% per year.

This paper develops a parsimonious static model for characterizing financing terms in collateralized lending markets. We characterize the systematic risk exposures for a variety of securities and develop a simple indifference-pricing framework to value the systematic crash risk exposure of the collateral. We then apply Modigliani and Miller's (1958) Proposition Two (MM) to split the cost of bearing this risk between the borrower and lender, resulting in a schedule of haircuts and financing rates. The model produces comparative statics and time-series dynamics that are consistent with the empirical features of repo market data, including the credit crisis of 2007-2008.

This case considers the valuation of Lin TV, a publicly-traded company with 30 TV stations. The case highlights how a change in operating strategy can enhance the firm's value, and considers the effect of consolidation within the industry on firm value.

This case can be used as a capstone valuation exercise for first-year MBA students in an introductory finance course. A senior associate in the business development group at American Cable Communications, one of the largest cable companies in the U.S., must prepare a preliminary valuation for acquiring AirThread Connections, a regional cellular provider. The acquisition would give American Cable access to wireless technology and the wireless spectrum and enable the company to offer competitive service bundles including wireless, currently a hole in the company's service offering. Students learn the basic valuation concepts including DCF (discounted cash flow) using APV (adjusted present value) and WACC (weighted average cost of capital) and they must choose the appropriate approach for situations in which the capital structure is changing or assumed to be constant. Students must consider the effect of constant debt versus the D/V (debt-to-value ratio) in estimating betas and the costs of capital. In addition, students analyze the effects of non-operating assets on valuation. As an additional assignment, instructors can require students to consider the personal tax disadvantage of debt as well as the synergies American Cable expects to achieve following the acquisition.

In February 2010, Jane Mendillo, CEO of Harvard Management Company, was reflecting on the list of issues facing Harvard University's endowment in preparation for the upcoming board meeting. The recent financial crisis had vividly highlighted several key issues including the adequacy of short-term liquidity, the effectiveness of portfolio risk management, and the balance of internal and external managers.

The leveraged loan market was in a crisis during the summer of 2007, following many years of low realized volatility (less than 4% per annum), an index of leveraged loans had fallen over 5% in the month of July. A sudden drop in capital market prices for an asset class can be caused by news affecting fundamental values; or by a widespread liquidity shock. The implication of a shock to fundamental value is that the price drop is permanent, whereas if the underlying cause of the price drop is caused by a liquidity event, the situation may represent a profitable investment opportunity. Investors must assess the likely cause of the recent price drops in the leveraged loan market and determine an appropriate investment strategy.

The Dynamic Markets course at Harvard Business School is organized around the hands-on application of financial decision making in a wide variety of capital market settings. The course relies heavily on in-class simulations of a range of market settings where students compete with their classmates for profits. The main pedagogical approach used in the course is what we call deriving by doing. The essential aspects of this pedagogy are dynamic decision settings, a strong reliance on competitive markets, and derivation of core concepts through active student decision-making. The upTick financial simulation software, developed at the Harvard Business School, is used to realistically recreate classic decision-settings in a competitive classroom setting. We convey the timing and uncertainty inherent in real-world finance problems by presenting the "case facts" sequentially (i.e., as they become available to the real-world decision maker), thereby allowing students to modify or reverse decisions as new information become available, and to respond strategically to the decisions of their competitors. Additionally, we clear student decisions in realistic capital markets, such that equilibrium outcomes are determined by competitive student interaction. Even though students participate in markets corresponding to a particular setting, the prices determined in the simulations are set by the participants and can depart from the historical prices within bounds set by the instructor.

In May 2006, a resident of Key West, Florida had to decide whether to renew his policy to insure against hurricane damage. The policy would cost $13,000 for one year, $5,000 more than what he paid in 2005. At the same time, a wealthy California resident was contemplating an opportunity to buy a "cat note" that offered a high yield, but with a chance of losing the full investment if severe hurricanes struck the coastline of the United States.

The goal of this simulation is to understand how convertible bonds can be viewed as a portfolio of simpler securities and to introduce an over-the-counter market. The convertible bonds that are available during the simulation are at-the-money and in-the-money so that changing credit risk exposure is not much of an issue. A convertible bond can be viewed as a simple coupon paying corporate bond plus a conversion option. A bond pricing model discounts the promised payments at a rate that compensates for time, risk, and expected loss (maturity matched Treasury yield plus a credit rating matched yield spread). The conversion option can be valued using the Black-Scholes call option pricing formula. The key is to recognize that each conversion option (one per bond) is equivalent to several equity call options (the conversion ratio determines how many equity options are implicit in each bond).

The goal of these simulations is to understand the dynamic replication technique behind the Black-Scholes/Merton options model. The simulations focus on a single stock and a risk-free discount bond, which are used to replicate a contingent payoff. The underlying stock and bond prices are randomly generated from the assumptions of the model, so that this simulation is testing the student's understanding and ability to use the model, rather than testing whether the model accurately explains prices. In each of the four simulations that make up this lesson, students are trying to replicate a contingent payoff, which is specified in terms of the closing stock price in one month (European-style derivative). The students are essentially working on an equity derivatives desk at a large bank and are responsible for delivering a derivative payoff to a client. The desk has taken in a premium upfront for guaranteeing the contingent payoff in one month's time. In the Black-Scholes/Merton model, a trader should be able to exactly match the contractual payment at expiration. Therefore, students are penalized based on the absolute difference between their actual ending value and a target ending value (starting value + derivative payoff). In particular, this difference is cumulated across all four simulations and then subtracted from their account.

The goal of this simulation is to understand the reliance of option values on volatility. When an investor trades an option, they are essentially trading volatility. Therefore, much of the focus in this lesson is on forecasting volatility. Students are able to use two primary methods for forecasting volatility in this lesson-historical and implied. Students are provided with a historical dataset, from which they can estimate historical volatility of the stock returns. They can also use the dataset to study various statistical relations between the securities. In particular, two of the three securities behave independently of the others. Thus, students are able to analyze the dataset to form views of how the security prices are likely to evolve relative to each other.

The goal of this simulation is to understand the patterns in index option prices that are not predicted by the Black-Scholes model. In particular, the simulation focuses on two properties of options prices. First, at-the-money implied volatilities from index options tend to be larger than the realized volatility. Second, the implied volatilities from index options are increasing as the strike price falls relative to the current index level (i.e., out-of-the-month call options have larger implied volatilities than at-the-money call options). Students are given a dataset of relevant market information to analyze. From these materials, students are expected to develop an investment strategy that attempts to deliver low-risk profits from the index options market. The actual simulation is fairly short and simple. Students trade 1-month put and call options on the S&P 500 (SPX) at three different strike prices (10% out-of-the-money, at-the-money, and 10% in-the-money). The simulation covers five months of calendar time (5 sets of options) in about 35 minutes.

This lesson develops the classical structural approach to pricing and hedging credit risk: Merton's (1974) contingent claims model of debt and equity claims. This model is used to make investment and risk management decisions in an over-the-counter (OTC) market for distressed bonds.

This lesson integrated Merton's (1974) contingent claims model of debt and equity claims with the CAPM, which allows us to examine the risks and pricing of credit portfolios and the derivative claims issued against them. In particular, this model is used to make investment and risk management decisions in the market for collateralized debt obligations (CDOs).

The goal of these simulations is to understand the mathematics of mean-variance optimization and the equilibrium pricing of risk if all investors use this rule with common information sets. Simulation A focuses on five to 10 years of monthly sector returns that are initially drawn from a known multivariate normal distribution. Mean-variance optimization is designed to produce the highest ratio of excess portfolio return to portfolio standard deviation (i.e. the highest Sharpe ratio) in this setting. Simulation B alters the setting by allowing students to determine expected returns through a simultaneous auction. We continue to have agreement over the covariance matrix, and implicitly over expected payoffs, but allow students to set market prices. The average portfolio weights across the 10 sectors is calculated and is used as the vector of market capitalization weights. With these market weights (w) and the given covariance matrix, the capital asset pricing model (CAPM) implied expected returns are calculated for each sector and compared with the student set expected returns.

The event arbitrage module includes two simulation sessions. The first simulation focuses on analyzing and evaluating individual merger transactions, while the second simulation emphasizes managing a portfolio of individual positions and the limitations of arbitrage investing in real-world capital markets. The underlying data and information are derived from actual merger transactions and have been disguised to prevent students from knowing the outcome ahead of time.

Describes a practical method for asset allocation that is more robust to estimation errors than the traditional implementation of mean-variance optimization with sample means and covariances. The Bayesian inspired Black-Litterman model is described after introducing the intuition of the Bayesian approach to inference in a univariate setting.

Investigates how prices are formed in competitive capital markets. Focuses on a single security called AOE. Students compete with computer traders and each other for market making and informed trading profits. Participants receive a variety of public news in the form of a research report on AOE, as well as subscriptions to news announcements and public quarterly earnings forecasts and releases. Participants also have access to costly private information in the form of one-week-ahead price targets for a per-use fee. The market structure is one with a centralized limit order book, but the ability to place limit orders is limited. The simulation of AOE is based on an actual security that has been disguised in time and industry to prevent students from anticipating the price path. All public news and contextual market information presented to students during the simulation correspond to actual information available to market participants in the real world at the time.

Covers how prices react to information, the incentives for bringing information into prices, and the paradox of market efficiency in equilibrium--for investors to work hard keeping markets efficient, they must always be somewhat inefficient at the margin. Uses separate financial market simulation software.

Demonstrates the Law of One Price in practice. Using synthetic securities, students should observe opportunities to earn profits when spreads emerge between portfolios that offer identical payoffs. Uses separate uptick financial simulation software.

The owner of Giant Cinema must decide whether to invest in a digital projector, a new technology for screening films, or purchase a traditional projector. The impact of the new technology is uncertain, and the case describes probabilities for different outcomes that students can incorporate in the financial analysis of the proposed project.

In January 2001, Mary Linn, vice president of finance for Ocean Carriers, a shipping company with offices in New York and Hong Kong, was evaluating a proposed lease of a ship for a three-year period, beginning in early 2003. The customer was eager to finalize the contract to meet his own commitments and offered very attractive terms. No ship in Ocean Carrier's current fleet met the customer's requirements. Mary Linn, therefore, had to decide whether Ocean Carriers should immediately commission a new capsize carrier that would be completed two years hence and could be leased to the customer.

Strategic Capital Management, LLC, is a hedge fund that is planning to make financial investments in Creative Computers and Ubid. Creative Computers recently sold approximately 20% of its Internet auction subsidiary, Ubid, to the public at $15 per share. Ubid's stock price closed the first day of trading at $48, giving Ubid a $439 million market capitalization. Paradoxically, the parent's stock price did not keep pace with that of its subsidiary. At the end of Ubid's first day as a public company, Creative Computers' equity value was less than the value of its stake in Ubid. The market prices implied that Creative Computers' non-Ubid assets had a value of negative $79 million. The relative prices and ownership link between Creative Computers and Ubid suggest a potential arbitrage opportunity. To evaluate how best to exploit this investment opportunity, Elena King, the manager of the hedge fund, must understand both the risks and expected returns associated with different long and short equity positions.

Ameritrade Holding Corp. is planning large marketing and technology investments to improve the company's competitive position in deep-discount brokerage by taking advantage of emerging economies of scale. In order to evaluate whether the strategy would generate sufficient future cash flows to merit the investment, Joe Ricketts, chairman and CEO of Ameritrade, needs an estimate of the project's cost of capital. There is considerable disagreement as to the correct cost of capital estimate. A research analyst pegs the cost of capital at 12%, the CFO of Ameritrade uses 15%, and some members of Ameritrade management believe that the borrowing rate of 9% is the rate by which to discount the future cash flows expected to result from the project. There is also disagreement as to the type of business that Ameritrade is in. Management insists that Ameritrade is a brokerage firm, whereas some research analysts and managers of other online brokerage firms suggest that Ameritrade is a technology/Internet firm. To obtain executable spreadsheets (courseware), please contact our customer service department at custserv@hbsp.harvard.edu.